Resource identification using historic well data

ABSTRACT

A method for identifying a resource in a field using historic well data including vertical well logs for the resource and historic horizontal well production data for the resource, the method including extracting a plurality of features from the vertical well logs, performing a spatial interpolation of the plurality of features extracted from the vertical well logs onto coordinates of the horizontal well production data to determine a plurality of interpolated features, and building a model predicting production of the resource in the field by regressing the horizontal well production data onto the interpolated features, wherein the model is displayed as a visualization of the resource production predicted in the field.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.14/599,902 filed Jan. 19, 2015, the complete disclosure of which isexpressly incorporated herein by reference in its entirety for allpurposes.

BACKGROUND

The present disclosure relates to methods for identifying naturalresources stored in the ground, and more particularly to a method foridentifying natural resources using historic well data.

Interest in shale plays previously exhausted by vertical wells, or brownfields, has grown with the advent of horizontal drilling techniques.These brown fields are believed to have potential if developed withhorizontal wells. In fact, several such brown fields are already beingdrilled using horizontal techniques with good results. However, withhigh drilling costs, the siting of extraction equipment for new wells at“sweet spots,” or sites with high potential, is important.

BRIEF SUMMARY

According to an exemplary embodiment of the present invention, a methodfor identifying a resource in a field using historic well data includingvertical well logs for the resource and historic horizontal wellproduction data for the resource, the method including extracting aplurality of features from the vertical well logs, performing a spatialinterpolation of the plurality of features from the vertical well logsonto coordinates of the horizontal well production data to determine aplurality of interpolated features, and building a model predictingproduction of the resource in the field by regressing the horizontalwell production data onto the interpolated features, wherein the modelis displayed as a visualization of the resource production predicted inthe field.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Preferred embodiments of the present invention will be described belowin more detail, with reference to the accompanying drawings:

FIG. 1 is a visualization of data for vertical and horizontal wellsaccording to an exemplary embodiment of the present invention;

FIG. 2 is a flow diagram of a method for predicting resource reservesaccording to an exemplary embodiment of the present invention;

FIG. 3 shows a visualization corresponding to FIG. 2, according to anexemplary embodiment of the present invention;

FIG. 4 is a flow diagram of a method for a functional PrincipalComponents Analysis (fPCA) for extracting features from well dataaccording to an exemplary embodiment of the present invention; and

FIG. 5 is a diagram of a computer system configured for predicting andvisualizing resource reserves according to an exemplary embodiment ofthe present invention.

DETAILED DESCRIPTION

According to an exemplary embodiment of the present invention, a set offeatures are automatically extracted from complex and high-dimensionalwell log curves, which are historic records from vertical wells, whereinthe extracted features are used to build one or more predictive models.These predictive models identify sweet spots in resource reserves (e.g.,shale plays) by associating the extracted features from historic welllogs of the vertical wells with production data from horizontal wells. Avisualization of the predictive model can be output for furtheranalysis.

Geological and petrophysical studies have shown that the identificationof sweet spots in shale plays involves finding locations with certaincombinations of parameters, such as thickness, TOC (total organiccarbon), maturity, porosity, and high gas-in-place. Exemplaryembodiments of the present invention leverage historic well data, whichis widely available for many locations (e.g., tens or hundreds ofthousands of data points for some reservoir fields), for an automatic,efficient, and robust method of identifying one or more sweet spots in agiven reservoir field One or more exemplary embodiments of the presentinvention use a principled framework for extracting simple andmeaningful features from complex and high-dimensional well log curves.

In the present disclosure the phrase “well log” refers to informationabout vertical wells and the phrase “well data” refers to informationabout vertical and/or horizontal wells. Further, it should be understoodthat production can be measured by any relevant method. For example, atypical unit of production is volume during a specified period, e.g., 6months of production, and can be measured as a number of barrels. Thespecified period can be any time period of interest (1-month, 3-month,6-month, etc.).

No method is known to exist for extracting such features fromhigh-dimensional and complex well log curves. Summary statistics such asmeans, maximum or minimum peak heights are too simple to capturerelevant features from the well log curves. Further, such summarystatistics are necessarily ad hoc, and would lead to variability inmodeling results. In contrast, according to one or more embodiments ofthe present invention, a principled statistical approach automaticallyextracts features according to a total variation criteria.

Exemplary embodiments of the present invention can be used to build oneor more predictive models related to the production of a naturalresource using historic production data and well logs (e.g.,petrophysical well logs or hydrophysical well logs) alone, without ageological core analysis. More particularly, according to one or moreembodiments of the present invention, a method includes extractingone-dimensional features from each of a plurality of well log curves,wherein the one-dimensional features are interpolated onto thecoordinates of the horizontal well production data using 2Dinterpolation (as opposed to more difficult 3D interpolations). This isadvantageous in situations where seismic data is not available. Byregressing historic production data from horizontal wells on theinterpolated extracted features from the vertical wells, production canbe directly predicted at new locations in the reservoir field. At leastin the case of petrophysical well logs, this has the advantage over 3Dinterpolations of the petrophysical properties that may or may notcorrelate well with production and may later need to be analyzed bygeological experts.

A workflow for predicting production (e.g., identifying sweet spots) ina given reservoir field is illustrated in FIGS. 1-3. In the example, thereservoir field includes a plurality of vertical wells associated withphysical well logs and a plurality of horizontal wells for whichproduction data is available.

Referring to FIG. 1, a reservoir field is given as a map 101 of verticalwells denoted by dots, e.g., 102, and horizontal wells denoted by lines,e.g., 103. Note that in FIG. 1, the horizontal wells are not located atthe same coordinates as the vertical wells. Well logs 104 are associatedwith each of the vertical wells and production data 105 is associatedwith each of the horizontal wells.

Each vertical well is associated with one or more well logs. Each welllog gives some physical measurement(s) along the depth of acorresponding well. The vertical well log data includes one or more ofthe following per depth: deep resistivity, gamma radiation, sonictransit time, density porosity, sonic porosity, neutron porosity, bulkdensity, and spontaneous potential. It should be understood that themethod may also be applied to other types of physical well log curves.

Each horizontal well is associated with historic production data (e.g.,oil, gas, or water production). As shown in FIG. 2, a method 200 forbuilding a predictive model includes receiving well data of a pluralityof vertical wells and a plurality of horizontal wells 201, thesystematic extraction of one-dimensional features from the well logs ofthe vertical wells using a functional Principal Components Analysis(fPCA) 202, determining a weighted average of the one-dimensionalfeatures across the vertical wells by kriging 203, and performing aregression of the historic production data from the horizontal wells onthe interpolated extracted features from the vertical wells 204, andoutputting the predictive model 205. Production prediction can beperformed on a grid across a portion of an entire production field usingthe predictive model, thus facilitating map visualization.

FIG. 3 is an exemplary visualization 301 showing production predictionon a grid across a portion of an entire production field and one or morepredicted sweet spots (e.g., 302 and 303). It should be understood thata sweet spot in the grid can be identified based on a threshold forproduction, either absolute or relative to the surrounding area.

The analytic workflow (FIG. 2) includes a model building phase and aprediction phase. As will be described below, the modeling phaseregresses interpolated principal components X onto the production y.This produces a model y=X*beta where beta is estimated in the modelingphase. The prediction phase chooses a new location where a prediction isdesired and interpolates the principal components onto that newlocation, wherein the interpolated value is X_new. The prediction can beachieved by determining y_new=X_new*beta (beta from the model).

The model building phase is illustrated by FIG. 2 and includes blocks202-204. Given the historic well data received at block 201, an fPCA isused at block 202 to extract one-dimensional features, which are theprincipal component scores, from each of the physical well log curves.Stated another way, K principal component scores are extracted for eachof the physical properties. The number K of principal components to beextracted for each physical curve can be chosen according to aproportion of the total variation in the curves explained by the first Kcomponents.

Theoretically, there are infinite principal component scores ρ_(i), i=1,. . . , ∞. The sum of the variances of these principal component scoresequals the (integrated) total variation in the curves, which is givenby: ∫var(x(t))dt=Σ_(i=1) ^(∞)var(ρ_(i)), wherein x(t) is a given curvedescribing a parameter of the well log. Therefore, by choosing all(infinite) principal component scores, 100% of the total variation inthe curves can be explained. In practice, the first K principalcomponents are chosen until the proportion Σ_(i=1)^(K)var(ρ_(i))/Σ_(i=1) ^(∞)var(ρ_(i)) is higher than some threshold,e.g. 80%. It should be understood that the threshold can be selected bya user for a specific application.

Referring to the fPCA at block 202 (FIG. 2 and FIG. 4), the physicalwell log curves (e.g., resistivity, sonic porosity, gamma radiation)include discrete observations x_(j), observed at discrete depths d_(j),j=1, . . . , p, where p is the number of depth units observed. Since thenumber of depth units can be in the thousands, and since thescatterplots (d_(j), x_(j)) demonstrate smooth functional relationships,this data can be represented as functions. This facilitates the use of aFunctional Data Analysis, which can be used to extract (functional)principal components from the curves. The functions can be representedusing a linear expansion:

${{x(d)} = {\sum\limits_{k = 1}^{K}{c_{k}{\varphi_{k}(d)}}}},$where φ_(k) (·) represent known basis functions (e.g., Fourier basis orB-spline basis functions). The coefficients c_(k) are calculated as partof the preprocessing of the data. This leads to an efficientcomputational representation of the discrete data as functional objects,where the data for each well have been reduced from thousands ofmeasurements to only K<<p coefficients (c_(k)) and known basis functions(φ_(k)). In an exemplary implementation, this can readily be achievedusing publically available statistical applications, such as the “fda”R-package (a language and environment for statistical computing andgraphics).

Assuming that the well logs have all been converted to functionalobjects x₁(·), . . . ,x_(n)(·) at 401, where n denotes the number ofvertical wells in the field, the fPCA includes finding principalcomponent weight function ξ₁(·) at 402 for which the principal componentscores:ρ_(1i)=∫ξ₁(t)x _(i)(t)dt,maximize Σ_(i)ρ_(1i) ² subject to:∫ξ₁ ²(t)dt=1.

At 403, the fPCA includes determining, sequentially, weight functionsξ_(k)(·), k≥2 such that Σ_(i)ρ_(ki) ² is maximized, whereρ_(ki)=∫ξ_(k)(t)x_(i)(t)dt are the k-th principal component scores. Thek-th corresponding weight function is additionally required to beorthogonal to all previously calculated weight functions, i.e.,∫ξ_(k)(t)ξ_(j)(t)dt=0 for all j=1, . . . , k−1. The computation offunctional principal component scores at 403 can also be performed usinga configured commercial statistical application.

The fPCA continues determining orthogonal weight functions until desirednumber of principal component scores is determined (see 404).

The model building phase further includes kriging at block 203, whichinterpolates the extracted features at the vertical wells onto thecoordinates of the horizontal wells. In one exemplary embodiment, sincethe horizontal wells extend outward, center coordinates of thehorizontal wells can be used. It should be understood that otherlocations along or near the horizontal wells can be used as thecoordinates, depending on user preferences.

Referring more particularly to block 203, let H and V denote a set ofhorizontal wells and a set of vertical wells, respectively. The k-thprincipal component score (for a given physical parameter) calculated atvertical well i∈V is denoted by x_(ik)(k=1, . . . , K). Krigingdetermines a weighted average of the principal component scores acrossthe set of vertical wells. More particularly, for a horizontal well i′and principal component score k, a weighted average is determined as:

$\begin{matrix}{{{\hat{x}}_{i^{\prime}k} = {\sum\limits_{i \in V}{{{weight}( {dist}_{i,i^{\prime}} )} \cdot x_{ik}}}},} & (1)\end{matrix}$where dist_(i,i′) denotes the distance between vertical well i∈V andhorizontal well i′. The kriging weights can be obtained by using aconfigured commercial statistical application.

According to an exemplary embodiment of the present invention, the modelbuilding phase further includes performing a Regression at block 204.Regression is a statistical process for estimating the relationshipsbetween dependent variables y and independent variables x. According toan exemplary embodiment of the present invention, the dependent variableis y_(i′), which denotes the production (e.g., 6-month production or anymeaningful summary of well production) at horizontal wells i′∈H, and theindependent variables are the interpolated principal component scores{circumflex over (x)}_(i′l), . . . , {circumflex over (x)}_(i′K) at thehorizontal wells i′∈H. The interpolated principal component scores areregressed on y_(i′) through a selected regression model (e.g., MultipleLinear Regression (MLR), Support Vector Machine, Neural Networks,LASSO). In one exemplary implementation, the MLR model is described by:y _(i′)=β₀+Σ_(k=1) ^(K) {circumflex over (x)} _(i′k)β_(k),   (2)where model parameter estimates {circumflex over (β)} are obtainedthrough least squares.

At block 204, by regressing historic production at the horizontal wellsonto the interpolated principal component scores, one or more predictivemodels are built that can be used to predict production at new sites inthe reservoir field.

Assume now, a user wishes to drill a new horizontal well i₀∈H at a newlocation (or on a grid of such new locations) whose spatial coordinatesare known. This is called the prediction phase (block 205) and involvesinterpolating the principal component scores onto the new site i₀ (or ona grid of such locations) through {circumflex over (x)}_(i) ₀_(k)=Σ_(i∈V)weight(dist_(i,i) ₀ )·x_(ik), using Kriging as describedherein. The estimated Regression model (e.g., the estimated MLR modelfrom (2)) is used to form the predicted production at the new horizontalwell(s):

${\hat{y}}_{i_{0}} = {{\hat{\beta}}_{0} + {\sum\limits_{k = 1}^{K}{{\hat{x}}_{i_{0}k}{{\hat{\beta}}_{k}.}}}}$

In view of the foregoing, once the model building phase is complete, aprediction of production can be performed at any given location in thereservoir field. In the prediction phase (block 205), the extractedprincipal components from the fPCA 202 are interpolated (using thekriging 203) onto the new locations, where predicted production isdesired. The estimated regression model from 204 is then used to predictthe production using the interpolated principal components as input tothe model. This prediction can be performed at any individual locationor on a grid across an entire field. In the case of an area wideprediction (e.g., for a portion of the entire reservoir field), the areacan be visualized with contour and/or color maps that provide the userwith a visual representation of predicted production across thereservoir field. This permits the user to identify locations with highproduction potential, i.e., sweet spots.

By way of recapitulation, according to an exemplary embodiment of thepresent invention, a method for identifying a resource in a field usinghistoric well data including vertical well logs for the resource andhorizontal well production data for the resource includes extracting aplurality of features from the vertical well logs, performing a spatialinterpolation of the plurality of features extracted from the verticalwell logs onto coordinates of the horizontal well production data todetermine a plurality of interpolated features, and building a modelpredicting production of the resource in the field by regressing thehorizontal well production data onto the interpolated features, whereinthe model is displayed as a visualization of the resource productionpredicted in the field.

In one or more embodiments, the method further comprises receiving thevertical well logs including at least one physical property measured ata plurality of depth, wherein each physical property includes one ofdeep resistivity, gamma ray, sonic transit time, density porosity, sonicporosity, neutron porosity, bulk density, spontaneous potential, andwherein each of the extracted features corresponds to one parameter. Inone or more embodiments, the vertical well logs each include data of atleast one physical property measured at a plurality of depth, andfurther wherein extracting the plurality of features from the verticalwell logs further comprises determining a plurality of functionalprincipal component scores for each of the physical properties from thevertical well logs. In one or more embodiments, the method furtherincludes receiving coordinates of a new location in the field without anexisting well, and predicting production at the new location using themodel, wherein the prediction of the production at the new location usesan interpolation of the functional principal component scores from thevertical well logs. In one or more embodiments, the model predictshorizontal well production.

In one or more embodiments, extracting the plurality of features fromthe vertical well logs further includes determining a plurality offunctional principal component scores as weighted integrals of curves inthe vertical well data, with weighting functions controlling a samplevariance of corresponding integrals across the vertical well logs.

In one or more embodiments, performing the spatial interpolation of theplurality of features from the vertical well logs onto the coordinatesof the horizontal well production data is performed by Kriging. In oneor more embodiments, method further includes describing the location ofthe horizontal well production data by center coordinates of thehorizontal well production data.

The methodologies of embodiments of the disclosure may be particularlywell-suited for use in an electronic device or alternative system.Accordingly, embodiments of the present invention may take the form ofan entirely hardware embodiment or an embodiment combining software andhardware aspects that may all generally be referred to herein as a“processor,” “circuit,” “module” or “system.”

Furthermore, it should be noted that any of the methods described hereincan include an additional step of providing a system for predicting andvisualizing resource reserves. Further, a computer program product caninclude a tangible computer-readable recordable storage medium with codeadapted to be executed to carry out one or more method steps describedherein, including the provision of the system with the distinct softwaremodules.

Referring to FIG. 5; FIG. 5 is a block diagram depicting an exemplarycomputer system 500 for predicting and visualizing resource reservesaccording to an embodiment of the present invention. The computer systemshown in FIG. 5 includes a processor 501, memory 502, display 503, inputdevice 504 (e.g., keyboard), a network interface (I/F) 505, a media(I/F) 506, and media 507, such as a signal source, e.g., camera, HardDrive (HD), external memory device, etc.

In different applications, some of the components shown in FIG. 5 can beomitted. The whole system shown in FIG. 5 is controlled by computerreadable instructions, which are generally stored in the media 507. Thesoftware can be downloaded from a network (not shown in the figures),stored in the media 507. Alternatively, software downloaded from anetwork can be loaded into the memory 502 and executed by the processor501 so as to complete the function determined by the software.

The processor 501 may be configured to perform one or more methodologiesdescribed in the present disclosure, illustrative embodiments of whichare shown in the above figures and described herein. Embodiments of thepresent invention can be implemented as a routine that is stored inmemory 502 and executed by the processor 501 to process the signal fromthe media 507. As such, the computer system is a general-purposecomputer system that becomes a specific purpose computer system whenexecuting routines of the present disclosure.

Although the computer system described in FIG. 5 can support methodsaccording to the present disclosure, this system is only one example ofa computer system. Those skilled of the art should understand that othercomputer system designs can be used to implement embodiments of thepresent invention.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Although illustrative embodiments of the present invention have beendescribed herein with reference to the accompanying drawings, it is tobe understood that the invention is not limited to those preciseembodiments, and that various other changes and modifications may bemade therein by one skilled in the art without departing from the scopeof the appended claims.

What is claimed is:
 1. A method for automatically visualizing a resourcein a field, the method comprising: recording horizontal well productiondata for the resource extracted using horizontal wells in the field;extracting a plurality of one-dimensional features from vertical welllogs comprising data gathered during a previous recovery of the resourceextracted using vertical wells in the field, wherein the vertical welllogs each include data of at least one physical property measured at aplurality of depths, wherein extracting the plurality of features fromthe vertical well logs further comprises determining a number offunctional principal component scores as weighted integrals of curves inthe vertical well data; performing a two-dimensional spatialinterpolation of the plurality of features extracted from the verticalwell logs onto coordinates of the horizontal well production data todetermine a plurality of interpolated functional principal componentscores by Kriging the functional principal component scores onto thecoordinates of the horizontal well production data; building a model ofthe resource at a plurality of locations in the field, wherein the modelincludes a function capturing a regression of the horizontal wellproduction data onto the interpolated functional principal componentscores; receiving coordinates of a new location in the field without anexisting well; and predicting production at the new location by Krigingthe functional principal component scores from the vertical well logsonto the new location using the function, wherein the model is displayedas a visualization of the resource in the field, and wherein thevisualization further includes the predicted production of the resourceat the new location.
 2. The method of claim 1, further comprisingreceiving the vertical well logs including at least one physicalproperty measured at a plurality of depths, wherein each physicalproperty includes one of deep resistivity, gamma ray, sonic transittime, density porosity, sonic porosity, neutron porosity, bulk density,spontaneous potential, and wherein each of the extracted featurescorresponds to one parameter.
 3. The method of claim 1, wherein theplurality of one-dimensional features extracted from the vertical welllogs are the functional principal component scores.
 4. The method ofclaim 1, wherein the function is applied to predict horizontal wellproduction at the new location given the one-dimensional features fromthe vertical well logs.
 5. The method of claim 1, where the functionalprincipal component scores explain a portion of a total variation in thevertical well logs, with weighting functions controlling a samplevariance of corresponding integrals across the vertical well logs,wherein the number of functional principal component scores iscontrolled by a threshold on the portion of the total variationexplained by the functional principal component scores.
 6. The method ofclaim 1, wherein the Kriging further comprises determining, for each ofthe horizontal wells, a value using the functional principal componentscores of the vertical wells, distances to each of the vertical wells,and a kriging weight on the distances, wherein a location of thehorizontal well production data used in calculating the distances isdescribed by center coordinates of respectives ones of the horizontalwells.
 7. The method of claim 1, wherein the visualization is displayedas a grid map across a portion of the field showing the predictedproduction of the resource at the new location in the portion of thefield.
 8. The method of claim 7, wherein further comprising: identifyingat least one sweet spot in the field using the predicted production ofthe resource at the new location and a threshold for predictedproduction; and distinguishing the at least one sweet spot in the gridmap.
 9. The method of claim 1, further comprising extracting theresource from the new location.
 10. The method of claim 1, whereindetermining the interpolated functional principal component scorescomprises determining, for each of the horizontal wells, a value usingthe plurality of functional principal component scores extracted fromthe vertical well logs, distances to each of the vertical wellsassociated with the functional principal component scores extracted, anda kriging weight on the distances, wherein a location of the horizontalwell production data used in calculating the distances is described bycoordinates of respectives ones of the horizontal wells.
 11. The methodof claim 10, wherein the regression of the horizontal well productiondata onto the interpolated functional principal component scorescomprises estimating a relationship between the horizontal wellproduction data for a given time period and the interpolated functionalprincipal component scores at the horizontal wells determined from theplurality of functional principal component scores extracted from thevertical well logs.
 12. A method for automatically visualizing aresource in a field comprising: recording vertical well logs for theresource extracted at a plurality of vertical wells in the field;recording horizontal well production data for the resource extracted ata plurality of horizontal wells in the field; extracting a plurality ofone-dimensional features, corresponding to a parameter measured withdepth, from the vertical well logs comprising data gathered during aprevious recovery of the resource extracted using vertical wells in thefield, wherein extracting the plurality of features from the verticalwell logs further comprises determining a number of functional principalcomponent scores as weighted integrals of curves in the vertical welldata; performing a two-dimensional spatial interpolation of theplurality of features extracted from the vertical well logs ontocoordinates of the horizontal well production data to determine aplurality of interpolated functional principal component scores byKriging the functional principal component scores onto the coordinatesof the horizontal well production data; building a model of the resourceat a plurality of locations in the field, wherein the model includes afunction capturing a regression of the horizontal well production dataonto the interpolated functional principal component scores; receivingcoordinates of a new location in the field without an existing well; andpredicting production at the new location by Kriging the functionalprincipal component scores onto the new location using the function,wherein the model is displayed as a visualization of the resource in thefield, and wherein the visualization further includes the predictedproduction of the resource at the new location.
 13. The method of claim12, wherein the visualization is displayed as a grid map across aportion of the field showing the predicted production of the resource atthe new location in the portion of the field.
 14. The method of claim13, wherein further comprising: identifying at least one sweet spot inthe field using the predicted production of the resource at the newlocation and a threshold for predicted production; and distinguishingthe at least one sweet spot in the grid map.
 15. The method of claim 12,wherein determining the interpolated functional principal componentscores comprises determining, for each of the horizontal wells, a valueusing the plurality of functional principal component scores extractedfrom the vertical well logs, distances to each of the vertical wellsassociated with the functional principal component scores extracted, anda kriging weight on the distances, wherein a location of the horizontalwell production data used in calculating the distances is described bycoordinates of respectives ones of the horizontal wells.
 16. The methodof claim 15, wherein the regression of the horizontal well productiondata onto the interpolated functional principal component scorescomprises estimating a relationship between the horizontal wellproduction data for a given time period and the interpolated functionalprincipal component scores at the horizontal wells determined from theplurality of functional principal component scores extracted from thevertical well logs.